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pro vyhledávání: '"A Chandak"'
Autor:
Adhikary, H., Adrich, P., Allison, K. K., Amin, N., Andronov, E. V., Arsene, I. -C., Bajda, M., Balkova, Y., Battaglia, D., Bazgir, A., Bhosale, S., Bielewicz, M., Blondel, A., Bogomilov, M., Bondar, Y., Bryliński, W., Brzychczyk, J., Buryakov, M., Camino, A. F., Chandak, Y., Ćirković, M., Csanád, M., Cybowska, J., Czopowicz, T., Dalmazzone, C., Davis, N., Dmitriev, A., von Doetinchem, P., Dominik, W., Dumarchez, J., Engel, R., Feofilov, G. A., Fields, L., Fodor, Z., Friend, M., Gaździcki, M., Gollwitzer, K. E., Golosov, O., Golovatyuk, V., Golubeva, M., Grebieszkow, K., Guber, F., Hurh, P. G., Ilieva, S., Ivashkin, A., Izvestnyy, A., Karpushkin, N., Kiełbowicz, M., Kireyeu, V. A., Kolesnikov, R., Kolev, D., Koshio, Y., Kowalski, S., Kozłowski, B., Krasnoperov, A., Kucewicz, W., Kuchowicz, M., Kuich, M., Kurepin, A., László, A., Lewicki, M., Lykasov, G., Lyubushkin, V. V., Maćkowiak-Pawłowska, M., Makhnev, A., Maksiak, B., Malakhov, A. I., Marcinek, A., Marino, A. D., Mathes, H. -J., Matulewicz, T., Matveev, V., Melkumov, G. L., Merzlaya, A., Mik, Ł., Morozov, S., Nagai, Y., Nakadaira, T., Naskręt, M., Nishimori, S., Olivier, A., Ozvenchuk, V., Panova, O., Paolone, V., Petukhov, O., Pidhurskyi, I., Płaneta, R., Podlaski, P., Popov, B. A., Pórfy, B., Prokhorova, D. S., Pszczel, D., Puławski, S., Renfordt, R., Ren, L., Ortiz, V. Z. Reyna, Röhrich, D., Rondio, E., Roth, M., Rozpłochowski, Ł., Rumberger, B. T., Rumyantsev, M., Rustamov, A., Rybczynski, M., Rybicki, A., Rybka, D., Sakashita, K., Schmidt, K., Seryakov, A., Seyboth, P., Shah, U. A., Shiraishi, Y., Shukla, A., Słodkowski, M., Staszel, P., Stefanek, G., Stepaniak, J., Świderski, Ł., Szewiński, J., Szukiewicz, R., Taranenko, A., Tefelska, A., Tefelski, D., Tereshchenko, V., Tsenov, R., Turko, L., Tveter, T. S., Unger, M., Urbaniak, M., Veberič, D., Vitiuk, O., Volkov, V., Wickremasinghe, A., Witek, K., Wójcik, K., Wyszyński, O., Zherebtsova, A. Zaitsev E., Zimmerman, E. D., Zviagina, A.
This paper presents the multiplicity of neutral and charged hadrons produced in 90 GeV$/c$ proton-carbon interactions from a dataset taken by the NA61/SHINE experiment in 2017. Particle identification via dE/dx was performed for the charged hadrons $
Externí odkaz:
http://arxiv.org/abs/2410.23098
Data heterogeneity has been a long-standing bottleneck in studying the convergence rates of Federated Learning algorithms. In order to better understand the issue of data heterogeneity, we study the convergence rate of the Expectation-Maximization (E
Externí odkaz:
http://arxiv.org/abs/2408.05819
From incorporating LLMs in education, to identifying new drugs and improving ways to charge batteries, innovators constantly try new strategies in search of better long-term outcomes for students, patients and consumers. One major bottleneck in this
Externí odkaz:
http://arxiv.org/abs/2407.03674
Consider $N$ players each with a $d$-dimensional action set. Each of the players' utility functions includes their reward function and a linear term for each dimension, with coefficients that are controlled by the manager. We assume that the game is
Externí odkaz:
http://arxiv.org/abs/2407.00575
Autor:
Grinsztajn, Nathan, Flet-Berliac, Yannis, Azar, Mohammad Gheshlaghi, Strub, Florian, Wu, Bill, Choi, Eugene, Cremer, Chris, Ahmadian, Arash, Chandak, Yash, Pietquin, Olivier, Geist, Matthieu
To better align Large Language Models (LLMs) with human judgment, Reinforcement Learning from Human Feedback (RLHF) learns a reward model and then optimizes it using regularized RL. Recently, direct alignment methods were introduced to learn such a f
Externí odkaz:
http://arxiv.org/abs/2406.19188
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion
Autor:
Flet-Berliac, Yannis, Grinsztajn, Nathan, Strub, Florian, Choi, Eugene, Cremer, Chris, Ahmadian, Arash, Chandak, Yash, Azar, Mohammad Gheshlaghi, Pietquin, Olivier, Geist, Matthieu
Reinforcement Learning (RL) has been used to finetune Large Language Models (LLMs) using a reward model trained from preference data, to better align with human judgment. The recently introduced direct alignment methods, which are often simpler, more
Externí odkaz:
http://arxiv.org/abs/2406.19185
We develop a remote patient monitoring (RPM) service architecture, which has two tiers of monitoring: ordinary and intensive. The patient's health state improves or worsens in each time period according to certain probabilities, which depend on the m
Externí odkaz:
http://arxiv.org/abs/2406.18000
Autor:
Goel, Krish, Chandak, Mahek
Retrieval-Augmented Generation (RAG) has revolutionized natural language processing by dynamically integrating external knowledge into Large Language Models (LLMs), addressing their limitation of static training datasets. Recent implementations of RA
Externí odkaz:
http://arxiv.org/abs/2406.09979
Autor:
Nie, Allen, Chandak, Yash, Yuan, Christina J., Badrinath, Anirudhan, Flet-Berliac, Yannis, Brunskil, Emma
Offline policy evaluation (OPE) allows us to evaluate and estimate a new sequential decision-making policy's performance by leveraging historical interaction data collected from other policies. Evaluating a new policy online without a confident estim
Externí odkaz:
http://arxiv.org/abs/2405.17708
Autor:
Nie, Allen, Chandak, Yash, Suzara, Miroslav, Ali, Malika, Woodrow, Juliette, Peng, Matt, Sahami, Mehran, Brunskill, Emma, Piech, Chris
Large language models (LLMs) are quickly being adopted in a wide range of learning experiences, especially via ubiquitous and broadly accessible chat interfaces like ChatGPT and Copilot. This type of interface is readily available to students and tea
Externí odkaz:
http://arxiv.org/abs/2407.09975